Ultrasound techniques are widely applied to medical imaging and measurements, where the research or practice of measuring blood flow velocity by using Doppler techniques has been deployed broadly in physics, engineering and clinics. In measurement of blood flow velocity by a continuous wave Doppler system, an ultrasound diagnostic system transmits an ultrasound into a sample volume within the human body, and receives the echoes scattered by the blood cells in the sample volume. Since the motion of the blood cells produces Doppler effect (the scattered echo has a certain frequency shift with respect to the transmitted wave and the shift is proportional to the moving speed of the blood cells), the blood flow condition in the vessels can be estimated by measuring the frequency shift. In measurement of blood flow velocity by a pulsed wave Doppler system, the system transmits pulsed waves repeatedly at certain intervals and receives the scattered echoes at a moment between two consecutive transmissions, and implements measurement of the flow velocity by measuring the change rate of the phase difference between different scattered echoes and a reference signal over time. In principle, measurement of flow velocity by pulsed wave Doppler techniques does not depend on Doppler effect, but the measured relationship between the signal frequency shift and the moving speed of the blood cells accords with Doppler effect. Therefore, processing of pulsed wave Doppler signals is generally similar to that of continuous wave Doppler signals in engineering and clinics.
After quadrature demodulation and filtering are performed on an RF ultrasound scattered echo signal, the spectrum of the Doppler signal is moved from several MHz to the audio range with bandwidth of several KHz centered at zero frequency (the signals thus formed in the range is generally referred to as audio Doppler signals, and termed as Doppler signals, for ease of illustration hereafter). Due to the viscosity of blood, blood flow velocity has a certain distribution within the vessels in the human body, being about 0 near the vessel walls and larger near the center of the vessels, and accordingly the spectrum of the Doppler signals has a width, where the maximum frequency is proportional to the maximum blood flow velocity in the vessels. Furthermore, the blood flow velocity within a vessel varies constantly with contraction and relaxation of the heart, so the spectrum of the Doppler signals detected varies constantly. Taking the Doppler signals at a particular moment for spectral analysis, we can estimate the blood flow condition in the vessels at this moment. If Doppler signals are taken at certain intervals for spectral analysis and the power of components with different frequencies is modulated with gray levels and displayed in order of time, the spectrogram of the Doppler signals can be obtained.
To diagnose disease of blood vessels by using the spectrogram for Doppler signals, some parameters are required to be extracted from the spectrogram, such as the average flow velocity, maximum flow velocity, minimum flow velocity, S/D (ratio of the maximum flow velocity during systole and that at the end of diastole), RI (resistive index), PI (pulsatility index) and so on. All of these parameters can be computed based on the envelope curve or average frequency curve for the spectrogram. The envelope curve for the spectrogram may be obtained by connecting the maximum frequencies of the Doppler signal spectrum at different moments, the amplitude of the envelope curve being proportional to the maximum blood flow velocity in the vessels. The maximum frequency is the basis for estimation. of the average frequency and computation of other Doppler parameters, and accordingly it is of great significance to estimate the maximum frequency accurately in clinical applications.
Conventional methods for estimating the maximum frequency is characterized in that, after an operator of a Doppler system determines that the spectrogram in the display meets the required characteristics, the spectrogram is frozen, the maximum frequency curve is manually plotted and then the average frequency or its associated parameters are automatically computed by some software tools. Its obvious disadvantage lies in poor repetition, low estimation accuracy and inability for real-time estimation. Due to influences from pulsate of the arterial blood flow and various noises, it has been very hard to estimate the maximum frequency in applications of spectral Doppler techniques. With rapid development of digital computing techniques, researchers have proposed many methods for estimating the maximum frequency, including PM (Percentile method), TCM (Threshold-crossing method), MTCM (modified threshold-crossing method), HM (Hybrid method), GM (Geometric method), MGM (Modified geometric method), ATM (Adaptive threshold method) and etc. These methods are generally used for spectrum estimation systems based on FFT (Fast Fourier Transformation), and alternatively may be used for other spectrum estimation systems. The above methods are explained and compared in Evans et al, Doppler Ultrasound: Physics, Instrumentation and Signal Processing. 2rd Edition, Chichester, UK: John Wiley & Sons Inc; 2000. The PM approach has a small computation amount but is subject to the influence from the SNR and bandwidth. In methods such as TCM, due to the presence of random noises whose spectral amplitude varies greatly, noise components with larger amplitude will easily be detected to lead to overestimation of the maximum frequency if the threshold is set too small. On the other hand, estimation of the maximum frequency tends to be small if the threshold is set too large (for details, please see “Comparison of four digital maximum frequency estimators for Doppler ultrasound”, Ultrasound Med Biol, Vol. 14, No. 5, pp 355-363, 1998). Some ATMs are proposed for use in existing commercial ultrasound imaging systems by Routh et al in U.S. Pat. No. 5,287,753 and “Evaluation of an automated real-time spectral analysis technique” (for details, please see Ultrasound Med Biol, Vol. 1, No. 1, pp 61-73) and Mo in U.S. Pat. No. 5,935,074, but the threshold has to be set larger in practice in order to increase the robustness for estimation of the maximum frequency. In ATM, the threshold value is set according to the SNR of the signals. Due to overlap of signals and noises in some spectrum, it will bring large errors and variances to estimation of SNR if signals and noises are discriminated by using threshold only, and thus accuracy and robustness for estimation of the maximum frequency are affected. GM has no problem in selection of threshold values and can implement automatic extraction of the maximum frequencies. But it takes the position of the selected peak of the power spectrum as the reference, and estimation of the spectrum by FFT has a large variance and the peak position tends to be random to a great extent, so it will affect the accuracy for estimation of the maximum frequency. MGM subtracts the line connecting the maximum point and the minimum point of the curve directly from the integral power spectrum curve, and the position of the maximum in the curve is estimated as the maximum frequency. Compared with others, this method has better robustness (for details, please see “The performance of three maximum frequency envelope detection algorithms for Doppler signals”, J Vasc Invest, Vol. 1, pp 126-134). But experiments show that this method also has the problem in underestimation of the maximum frequency when the signal spectrum is relatively wide.
FIG. 1 is a flowchart showing the typical processing of Doppler signals. First, the echo signals received by the ultrasound probe are beam formed, demodulated, filtered and AD (Analog-to-Digital) converted to obtain digital Doppler signals and then spectral analysis is performed on the Doppler signals by using FFT or the like to obtain a spectrogram. Next, the technique such as MGM, ATM or the like is applied to the spectrogram to estimate the maximum frequency of the Doppler signals, so as to extract the envelope curve of the spectrogram. Finally, the Doppler parameters for representing the Doppler signals are calculated according to the characteristic points on the envelope curve.
The above prior arts have a main disadvantage in that the maximum frequency tends to be underestimated, particularly when the spectrum is relatively wide. Further, TCM has a disadvantage in that setting of threshold parameters has too much influence on estimation of the frequency, which causes the computation results of the Doppler parameters to be less accurate.